論文

Bullying is a serious problem, especially at elementary school and junior high school and occurs all over the world with some differences. As a countermeasure against bullying, many interventions and preventive programs were proposed (e.g., counseling, dispatch of advisers, group work, suspension order) and there are many researches about analysis of bullying and intervention and preservation strategies. However, a lot of samples and long times are required for analysis of the strategies in the real world. And we believe simulations of the bullying and the countermeasures are effective as a quick and easy analysis. In this paper, we developed a simulation model of school bullying "within-group" or "within-class" that is based on "Socion theory". The primary feature of "Socion theory" is that each agent has an imagined personal relationship. We implemented a strategy named conforming strategy to simulate the bullying caused by conforming behaviors of students. The student agents who implement the conforming strategy watch the human relationship around them and update their own imagined relationships. Then they conform to other students to get good positions or keep their grounds on the basis of simulation results based on their own imagined relationships. The result of the experiment shows the conforming strategy increases the probability of bullying and the number of victims, and conforming behaviors increase the probability of bullying in the real world. We also show a state that a teacher underestimates or cannot perceive bullying when it occurred, and many teachers underestimate or cannot perceive bullying in the real world. Totally, we show that "Socion theory" has expressiveness to use as a foundation of bullying simulation.

In this paper, we propose to make intention drawing from system to human and collecting task between human and empty-box just by behaviors with two axis actuators. First, we extracted elements of minimum interaction model between user and empty-box using immersion discovering method based on Wizard of Oz method. Next, we designed three-state model based on findings. Third, we implemented three-state model by motion capture system, and confirms how collecting task was achieved. Last, we evaluated these models are indistinguishable from human manipulation using Turing-test based evaluation.

We propose an alternative approach to find each robotic agent's unique interaction strategies, called Immersing Discovery Method. In this approach, the human manipulator behaves as if she/he becomes the robot and finds efficient interaction strategies based on each robot's shape and modalities. We implement the system including a reconfigurable body robot, an easier manipulation system, and a recording system to evaluate the validity of our method. We evaluate a block-assembling task by the system by turning on and off the modality of the robot's head. Subsequently, the robot's motion during player's motion significantly increases whereas the ratio of confirmatory behavior significantly decreases in the head-fixed design. This result suggests that the robot leads the users and the user follows the robot in head-fixed condition not as in a normal turn-taking interaction style of the head-free condition.

In this article, we propose a model for the addition of user information to sensor data. The problem is that it is difficult to identify a user of objects that are used by multiple people and to define a unique way to identify the user. The proposed model identifies the user assuming that the objects are used by multiple people. Further, it introduces three policies. By our experiment, we have proved the following: (1) Addition of user information to sensor data by the model is valid. (2) By presenting a difference in policies, it is possible to draw someone's attention.

本研究では,発話音声,表情パーツである目や口などの表出要素の組合せによりユーザと擬人化ロボット間のインタラクションを改善させることを目的としている.ロボットの外見を検討した関連研究は存在するが,共通の土台に各表出要素を組み合わせ,ユーザとのインタラクションにおける各表出要素の影響を評価した研究は存在しない.本研究ではインタラクションを改善する表出要素を検討するため,各表出要素を組み合わせた台車ロボットとユーザがインタラクションを行う対人実験を2種類実施した.各表出要素は台車ロボットがユーザに返答する時間SRTに対するユーザの許容度を測ることで評価した.一つ目の対人実験では,音声とディスプレイ,目パーツの組合せにより各4条件をそれぞれ比較し,発話と外見どちらの表出要素にユーザは影響を受けるか検討した.二つ目の対人実験では,目と口パーツの組合せにより2×2条件を比較し,目と口のどちらの表出要素がインタラクションを改善するか検討した.結果として,擬人的な外見を与えるかどうかではなく,発話の有無によりSRTに対するユーザの評価は影響を受けること,また目が口よりもSRTに対するユーザの許容度を大きくすることが分かった.

本研究では，物体に対し擬人的な入出力デバイスを取り付け，物体全体を擬人化し，物体に直接ふれ合いながら情報提示を行なう直接擬人化手法を提案する．このため我々は，対象となる物体に対し，後から検知領域の側面に取り付ける事で，物体の表面に仮想的な接触検知領域を実装する「指示区画検知センサ」を開発した．指示区画検知センサは検知領域の側面に取り付けて二次元平面を検知する事が可能であり，簡易に取り付け可能で，物体の要素を隠さず，人間の手の接触のみに反応した応答が可能となる．本研究では，指示区画検知センサの精度改善のため，ビタビアルゴリズムを用いたユーザの手の指示位置を推定する手法を考案した．これにより，センサから40cmの二次元検知領域内に侵入した人間の手を，平均して80％の精度で検知する事が可能となった．さらに，開発した指示区画検知センサを他の擬人化パーツと連携させ，物体を擬人化した機能説明をおこなった．その結果，直接擬人化手法による情報提示が円滑に行なわれる事が確認できた．

We propose a new approach to human-robot interaction (HRI) in which a common target object is anthropomorphized using attachable humanoid parts. With this approach, the user perceives that the target has an intention and senses what the imaginary body of the target looks like through the attached body parts. We experimented how users accepted the intentions and imaginary body image of a common target object using the humanoid parts. We also compared the resulting HRI with that in the case of the general communication robot Robovie to demonstrate the possibilities our proposed method offers. The results indicated that an anthropomorphized target object can interact with users through this approach. Furthermore, in comparison to interaction with an independent communication robot, with this approach, users remembered the functions of the anthropomorphized target to a greater extent and were more intimate with it.

We propose a direct anthropomorphization method that agentize an artifact by attaching anthropomorphic parts to it. There are many studies to provide information to users using spoken directions and gestures via anthropomorphic agents such as CG agents and communication robots. Our method directly anthropomorphize the artifact through robotic bodily parts shaped like those of humans. The anthropomorphized artifact using these parts provides information to people by giving them spoken directions and expressing themselves through body language. Using these devices, people are able to accept more attentions to the artifact, than using anthropomorphic CG or robot agents. We conduct an experiment to verify a difference between explanation of fuctions of the artifact using direct anthropomorphization method and explanation of them using independent humanoid-agent "Robovie". The results of participants' questionnaires and gazes during the experiment indicate that they noticed to the target artifact and memorized functions using direct anthropomorphization method more than using independent humanoid-agent.

We propose an anthropomorphization framework that determines an object’s body image. This framework directly intervenes and anthropomorphizes objects in ubiquitous-computing environments through robotic body parts shaped like those of human beings, which provide information through spoken directions and body language. Our purpose is to demonstrate that an object acquires subjective representations through anthropomorphization. Using this framework, people can more fully understand instructions given by an object. We designed an anthropomorphization framework that changes the body image by attaching body parts. We also conducted experiments to evaluate this framework. Results indicate that the site at which an anthropomorphization device is attached influences human perception of the object’s virtual body image, and participants in experiments understood several instructions given by the object more clearly. Results also indicate that participants better intuited their devices’ instructions and movement in ubiquitous-computing environments.

This study delves into the dynamics of human-agent interaction (HAI), specifically focusing on how communication patterns and personalities (both users' and virtual ones) influence user preferences of Embodied Virtual Agents (EVAs). Under the Big Five personality framework, we designed two personality-wise opposite EVAs - a shy SA and a confident CA. We then examine such effect of communication pattern and personality matching on user preferences. The experiment involved an online survey with 87 participants, consisting of questionnaires, video evaluations, and interactive tasks. Results demonstrated significant differentiation in the agents' perceived personalities and revealed user preference for SA, although the reasons remain to be elucidated. The preliminary findings on the simultaneously effective factors of similarity-attraction principle and complementary-attraction principle are unprecedented and will be further investigated in our future work.

In this study, the authors conducted Hanabi experiments under four conditions: three conditions in which humans played Hanabi using a UI that could present the order in which to give hints, as well as a UI that could indicate that a situation was troubling, in addition to the usual Hanabi game UI; and one condition in which an AI agent and a human played Hanabi. As a result, it was found that in conditions where the order of giving hints was made clear and humans could communicate their concerns, humans took less risky actions and improved their scores compared to the normal UI. This shows that the slight clues that have been overlooked in previous research were important for cooperative behavior. In addition, it was also found that the combination of agents and humans tended to show higher scores than the combination of humans.

Currently, the development of the metaverse lies in the hands of industry. Citizens have little influence on this process. Instead, to do justice to the pluralism of (digital) societies, we should strive for an open discourse including many different perspectives on the metaverse and its core technologies such as AI. We utilize a participatory speculative design (PSD) approach to explore Japanese citizens’ perspectives on future metaverse societies, as well as social and ethical implications. Our contributions are twofold. Firstly, we demonstrate the effectiveness of PSD in engaging citizens in critical discourse on emerging technologies like the metaverse by presenting our workshop framework and participants’ processes. Secondly, we identify key themes from participants’ perspectives, providing insights for culturally sensitive design and development of virtual environments. Our analysis shows that participants imagine the metaverse to have the potential to solve a variety of societal issues; for example, breaking down barriers of physical environments for communication, social interaction, crisis preparation, and political participation, or tackling identity-related issues. Regarding future metaverse societies, participants’ imaginations raise critical questions about human-AI relations, technical solutionism, politics and technology, globalization and local cultures, and immersive technologies. We discuss implications and contribute to expanding conversations on metaverse developments.

Currently, the development of the metaverse lies in the hands of industry. Citizens have little influence on this process. Instead, to do justice to the pluralism of (digital) societies, we should strive for an open discourse including many differen

In 2023, we proposed a hypothetical knowledge management framework that incorporates science fiction prototyping (SFP) into the traditional roadmapping (RM) method to make RM more innovative and create a future beyond what we can currently imagine. Our main objective is to create a new organizational knowledge co-creation method for generating innovative ideas by incorporating humanistic factors utilizing SFP into RM. In the current article, we present the exploratory study we conducted to examine the hypothetical framework. Experimental workshops were held in November 2023 to compare the integrated method, in which SFP is applied to RM, with the original RM method. We followed a crossover design in the workshops and analyzed the results qualitatively and quantitatively following the perspectives proposed in the hypothetical framework. The qualitative analysis revealed successful integration regarding diverse and broader perspectives. The quantitative analysis utilizing Wilcoxon signed rank tests showed that the scores of the integrated method were significantly higher than those of RM for variables evaluating the effectiveness of the integration, while not for variables related to participants' feelings and satisfaction with the workshops, and sustainability. These findings indicate that the integration was successful in terms of its effectiveness as a method, while participants' subjective feelings toward the workshops were not different in both methods. As for sustainability, which requires a long-term perspective, no apparent differences were observed in these short-term workshops.